The invention relates to electrocardiography (ECG), and more particularly to an improved electrocardiographic apparatus and method for predicting potential ventricular tachycardia and other forms of cardiac arrhythmia by the analysis of all ECG segments and intervals through the use of computer-supported analysis of selected signal components in ECGs, wherein, after preamplification and impedance transformation, signals are incrementally amplified, normalized, analog to digital converted, stored in memory, and manipulated by computer to provide frequency information about selected components of the signals.
After a myocardial infarction (M.I.) patients are at risk from the occurrence of dangerous disturbances of the cardiac rhythm, ventricular tachycardia. Sudden death from acute cardiac arrhythmia, in particular ventricular tachycardia, is a major risk in the first few hours after a myocardial infarction. During the first days after a myocardial infarction the incidence of ventricular arrhythmia is approximately 90%. The incidence of arrhythmias decreases considerably after the first several days but is still a substantial risk to the myocardial infarct patient. Five to ten percent of the post-infarct patients die within one year from sudden rhythmogenically caused cardiac death. Statistically, without treatment, approximately 50% of all myocardial infarct patients will eventually die of ventricular tachycardia.
Previous methods for identifying this risk group, e.g., long-time or Holter ECG, exercise test, resting ECG either are not sensitive enough or not specific enough. In the 1970s, minute signal fluctuations (of about 1 to 5 .mu.V amplitude) were discovered in the surface ECG during sinus rhythm at the end of the QRS complex. These so-called late potentials occur significantly more frequently in post-infarct patients at risk of disturbance of rhythm than in patients with a good prognosis.
U.S. Pat. Nos. 4,422,459 to Simson teaches that a series of successive ECG waveforms may be captured, converted into digital form, and averaged together (after the exclusion of abnormal or non-typical waveforms) to provide a relatively noise-free composite waveform. Simson filters this averaged waveform using reverse or bi-directional filtering with a high-pass filter having a corner frequency of 25 Hz. Simson then computes the root-mean-square (RMS) value of the voltage in the filtered tail segment of QRS complex. In addition, Simson measures the width of the QRS complex after the filtering. Simson uses the RMS voltage measurement and the QRS width measurement as indications of whether or not the patient is likely to be subject to ventricular tachycardia.
The Simson method may be inapplicable to patients suffering from bundle branch block. It cannot always distinguish between meaningful high frequency components at the trailing edge of the QRS complex, which may indicate a predisposition to ventricular tachycardia, and noise that originates from power line disturbances or skeletal or smooth muscles or the like.
To extract more useful diagnostic information from the ECG signal, researchers have utilized Fourier transformations to process ECG waveforms. See, e.g., Cain, M. E., et al., "Quantification of Differences in Frequency Content of Signal-Averaged Electrocardiograms in Patients With Compared to Those Without Sustained Ventricular Tachycardia." Vol. 55, American Journal of Cardiology, 1500 (Jun. 1, 1985), showing frequency domain representations of the ECG X, Y and Z lead signals from patients with and without ventricular tachycardia. Energy, expressed as voltage squared, is plotted against frequency. The Cain et al. analysis was limited to a single Fourier transformation of a lengthy segment that included both the 40 millisecond terminal portion of the QRS complex and also the entire ST part of an averaged ECG waveform preprocessed with a Blackman-Harris window function to minimize spectral leakage. Cain, et al.'s analysis of the frequency domain data was limited to the frequency range of from 20 Hz to 50 Hz. Cain, et al.'s rationale for performing Fourier analysis of such an extended length segment, treating it as a single unit, was to "enhance frequency resolution."
Frequency domain techniques for locating small potentials often contain obscured voltages due to the relative size of the QRS complex in the ECG waveform. With Fourier transform techniques even small segments containing little information must also be windowed, further reducing information content and yielding a frequency domain representation that has too low a resolution to be useful in the determination of the presence or absence of micropotentials of a selected frequency range. Adapted autoregressive mathematical models for calculating the frequency power spectrum have been used for ECG signal frequency analysis. These mathematical methods enable an analysis to be performed by means of the so-called "maximum entropy method" (MEM), or by means of the so-called "adaptive filter determination" (AFD) which is related to the so-called "fast adaptive forward/backward least-squares method." See, Haberl et al. U.S. Pat. No. 5,211,179. Such methods were developed for calculating the optimum order of the autoregressive model and eliminating interfering low-frequency fundamental oscillations. The aforementioned signal processing methods are particularly well adapted for the analysis of ECGs.
U.S. Pat. No. 5,109,862 to Kelen et al. discloses a system which can receive X, Y and Z lead electrocardiographic signals, digitize the signals and signal average them. The second derivative or "acceleration" of the signals is then derived and a Blackman-Harris window is applied to the resulting vector, which is then operated upon by a fast Fourier transform (FFT) to yield a spectrum. Kelen et al. process the terminal portion of the QRS signal to attempt to find late potentials and rely upon samples of overlapped time segments. A "spectral entropy" calculation is performed, which in some sense is related to the topography of pseudo three-dimensional spectral plots. The teachings of Kelen et al., however, are not directed to measuring the intra-QRS signals per se.
U.S. Pat. No. 5,092,341 to Kelen discloses a system related to the system disclosed of Kelen et al., supra. The Kelen system is directed to addressing the limitation of prior late potential analysis systems, particularly time domain systems such as that of Simson. Kelen discloses that arrhythmogenic abnormalities are indicated by frequent and abrupt changes in the frequency spectrum of the QRS wavefront velocity as it propagates throughout the ventricle around regions of abnormal conduction thereby resulting in a high degree of spectral turbulence. A high degree of spectral turbulence of the overall QRS signal during sinus rhythm is considered indicative of an anatomic-electrophysiologic substrate for reentrant tachyrhythmia regardless of the detection of late potentials in the terminal QRS region of the digitized averaged electrocardiographic signal. Observations, measurements and calculations are made generally upon the QRS complex as a whole and not upon any portion arbitrarily identified by a temporal frequency or amplitude characteristic.
U.S. Pat. No. 5,271,411 to Ripley et al. discloses a system for detecting premature ventricular contractions and quantifying cardiac arrhythmias. Ripley et al. include a plurality of channel connectors connected to a multiplexer that feeds a filter bank with analog electrocardiographic signals. Generally, by extracting morphology information from an electrocardiographic signal in order to group heartbeats into similar classes information for morphologically similar beats can be used to assist in determining whether a given beat is normal or an abnormal beat, such as a premature ventricular contraction.
U.S. Pat. No. 5,199,438 to Pearlman discloses a microcomputer-based system for determining cardiac power. Pearlman determines cardiac the pumping power in terms of the time rate of change of mechanical energy developed by the left ventricle of a human heart. The system includes an occlusive cuff blood pressure monitoring system, a gamma camera, an echocardiogram system, an electrocardiograph system, and a Doppler ultrasound blood flow sensor all providing signals to a microcomputer. Pearlman defines cardiac power as the time derivative of the product of cardiac volume and cardiac or aortic pressure. A cardiac power index is the slope of the portion of the power-versus-time curve from the onset of systole to the moment when maximal power occurs. The cardiac power index is used to determine ventricular performance during the ejection portion of the cardiac cycle.
U.S. Pat. No. 4,680,708 to Ambos et al. discloses a frequency domain technique for late potential analysis using the Fourier transform on a signal segment in the terminal QRS region. Areas under the high and low frequency bands are calculated from the resulting spectral curve, and area ratios are determined.
Ambos et al. and Cain et al., have more recently attempted to study single-segment FFT results of selectively band-filtered signal averaged electrocardiographic (SAECG) signals from the entire cardiac cycle. They addressed the problem of extracting information on micropotentials within QRS complexes, typically obscured by the relatively very large and high slew-rate QRS morphology, by applying digital band-pass filters. Their frequency range of interests, however, have been confined to 70-128 Hz and much lower.
U.S. Pat. No. 5,215,099 to Haberl et al. teaches spectral temporal mapping (STM) of windowed Fourier transforms on overlapping segments of the terminal QRS, and early ST region for late potential analysis in the frequency domain. The result is a pseudo 3-dimensional time-frequency-power spectral density (PSD) display. It has been shown that STM increases the sensitivity and specificity of the SAECG when used in conjunction with standard time domain techniques i.e., the Simson method. However, STM may be weak in reproducibility due to high noise sensitivity and the dependence of the resulting STM on precise QRS end-point determination.
In spectral turbulence analysis (STA), Kelen calculated the velocity of the SAECG data and then used the short-time Fourier transform on multiple overlapping segments, including segments within the QRS complex. As in Haberl's STM technique, the result was also a 3-dimensional time-frequency-power spectral density (PSD) display. However, this technique is handicapped in that the short time lengths offer limited frequency resolution, especially after multiplication by window functions. Furthermore, the calculations necessary to quantify "spectral turbulence" are relatively complex, and the resulting indices are difficult to comprehend when the resulting 3-dimensional maps are referenced visually.
It has been found that slurs and notches within the QRS complex were strongly correlated with the presence of myocardial infarction and coronary artery disease. Flowers et al., strengthened this association further by correlating QRS notching and anatomic identification of infarct scar. See, e.g., Flowers et al. "The Anatomic Basis of High Frequency Components in the Electrocardiogram", Circulation, 1969; 39:531, and Flowers et al. "Localization of the Site of Myocardial Scarring in Man by High-Frequency Components", Circulation, 1969; 40:927. Historically, narrow band analog filters were used in an attempt to characterize the frequency bands of normal and notched QRS complexes.
Atrial fibrillation is thought to be due to reentrant pathways in the atria. A patient who has atrial flutter or fibrillation is at risk for cerebrovascular accident or stroke, which is often disabling and potentially fatal. For atrial fibrillation to take place, the atrial myocardial substrate also requires areas of slow conduction to initiate and maintain the reentrant circuit. The non-invasive risk assessment of atrial fibrillation has recently been studied using P wave-triggered or aligned SAECG. Only the time domain approach has been utilized, primarily because of the lack of commercially available signal processing packages for frequency domain analysis of the P wave. The main criterion has been reported by Guidera and Steinberg and Fukunami et al, as well as other independent researchers, to be the prolonged signal-averaged total filtered P wave duration. See, e.g., Guidera, S. A., Steinberg, J. S., "The Signal-Averaged P Wave Duration: A Rapid and Noninvasive Marker of Risk of Atrial Fibrillation," J Am Col Cardiol, 1993; 21:1645-51, and Fukunami M, et al. "Detection of Patients at Risk for Paroxysmal Atrial Fibrillation During Sinus Rhythm by P Wave-Triggered Signal-Averaged Electrocardiogram," Circulation, 1991; 83:162-169. No filtering standards have yet been established, hence the time domain criteria suggested by these authors are specific to their techniques used. A prolonged P wave duration in the time domain is thought to be a non-invasive indicator of the presence of delayed activation in the atria.
In patients with mitral stenosis or Wolff-Parkinson-White syndrome, it is difficult to determine the P wave endpoint for time domain analysis. Yamada and Fukunami et al., therefore had reported a spectral method for P wave analysis using area ratios, similar to the method that Cain and Ambos used for late potential analysis. See, Yamada T, et al. "Characteristics of Frequency Content of Atrial Signal-Averaged Electrocardiograms During Sinus Rhythm in Patients with Paroxysmal Atrial Fibrillation," J Am Col Cardiol, 1992; 19:559-63. They did not perform their analysis beyond 50 Hz. However, their technique has not been used in a widespread fashion, and had lesser sensitivity and specificity than time domain P wave analysis results.
Present methods of late potential analysis utilize both time and frequency domain methods. Existing frequency domain techniques require sensitive determination of the QRS endpoint, or derivation of several statistical indices derived from complex mathematical algorithms involving multiple interslice correlation statistics of "spectral turbulence" information. However, all such techniques are not as well accepted as the conventional time domain Simson method, when comparing reproducibility, sensitivity and specificity of the results. The time domain method is not without limitations either. It cannot be used to analyze the SAECG signals in patients with conduction delay problems, and has a low positive predictive accuracy.
Delayed activation and abnormal recovery is well documented in the study of "late potentials." They are described as surface manifestations of alterations in conduction patterns through necrotic or fibrotic heart tissue in post-myocardial infarction patients at risk for sudden cardiac death. Fragmented conduction patterns should not manifest in SAECG signals acquired from patients without previous myocardial damage, because healthy heart tissue would form a more homogenous electrical conduction substrate compared with damaged tissue. In the latter case layers of damaged or necrotic tissue would break up or "diffract" the electrical conduction wavefront and form small amplitude, high frequency "interference patterns" along the conduction pathway. Having this type of tissue substrate predisposes the patient to experiencing life threatening re-entrant ventricular arrhythmias. It is believed that atrial flutter and fibrillation also stems from a substrate supportive of reentrant pathways. Such patients are associated with a high incidence of cerebrovascular thromboembolism, i.e. stroke.
In the past ten years, the SAECG has emerged as an increasingly valuable non-invasive tool for evaluating patients with known or suspected ventricular arrhythmias. Simson initially described the use of SAECG to determine the presence of late potentials as small amplitude, high frequency signals in the terminal QRS and early ST segment of the SAECG. They are thought to correspond with delayed cardiac activation, and are believed to indicate the presence of an underlying myocardial substrate for reentrant ventricular arrhythmias. Simson initially found late potentials in post-myocardial infarction patients with sustained ventricular tachycardia. It has also been shown that the presence of late potentials is indicative of inducible ventricular tachycardia during electrophysiology study, as well as other post-myocardial arrhythmic events. Signal averaging has also been used to study patients presenting with syncope of unknown origin. Furthermore, signal averaging has been used in conjunction with left ventricular ejection fraction (LVEF) tests to manage survivors of acute myocardial infarction. If the patient's LVEF&gt;40%, and SAECG time domain results are negative, then further expensive electrophysiology testing is not recommended for the patient. The question is what happens if the SAECG test is positive, i.e. how accurate is the test in predicting patient risk for developing either spontaneous or inducible, sustained ventricular tachycardia? While the SAECG time domain results have a very high negative predictive value, they unfortunately also have a low positive predictive value.